Abstract A new bioinspired Battery Thermal Management System (BTMS) for electric cars is presented in this work. It is based on the microstructure of the wings of the Morpho didius butterfly. The thermal performance of a conical frustum fin design was assessed using transient thermal and CFD simulations in three different materials: graphite, copper, and aluminium. The biomimetic casing is combined with machine learning (ML) and deep learning (DL) models, such as XGBoost, Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Linear Regression, in contrast to previous methods, to replace iterative simulation cycles. With a prediction accuracy of R 2 = 0.994 and RMSE = 0.098 °C, the CNN model was able to predict temperature gradients and convective heat flux under a variety of design conditions. The AI-driven system achieved design optimization speed improvements by decreasing repeated CFD computations through the replacement of about 50 iterative CFD design evaluations with less than 10 validation simulations after training the surrogate model. 3D plots, hybrid heatmaps, and residual maps are examples of advanced visualisation approaches. This work creates a scalable basis for applications in next-generation thermal systems by utilising CNN-enabled surrogate modelling for the first time on a Morpho-inspired EV case.
Hussain et al. (2026) studied this question.